Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test") - Notebooks
- Google Colab
- Kaggle
Whisper Tunisien
This model is a fine-tuned version of openai/whisper-medium on the comondov dataset. It achieves the following results on the evaluation set:
- Loss: 3.2714
- Wer: 106.1818
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2223 | 5.2083 | 500 | 2.8313 | 106.8364 |
| 0.0126 | 10.4167 | 1000 | 3.2714 | 106.1818 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for Arbi-Houssem/Tunisian_dataset_STT-TTS20s_test
Base model
openai/whisper-mediumEvaluation results
- Wer on comondovself-reported106.182